CN106919674A - A kind of knowledge Q-A system and intelligent search method built based on Wiki semantic networks - Google Patents
A kind of knowledge Q-A system and intelligent search method built based on Wiki semantic networks Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses a kind of knowledge Q-A system and intelligent search method built based on Wiki semantic networks, it is related to the technical fields such as text semantic analysis, intelligent retrieval and machine learning.The present invention orients the semi-structured text data of crawl by vertical field Wiki, follow predefined ontologies and extract Knowledge Element automatically, and then set up Ontology using deep learning Algorithm for Training and understand model, realize the paradigmatic relation of Knowledge Element and the semantic understanding of derivation relationship.The present invention combines automatic theme reasoning and expertise orientation two advantages of aspect of optimization that machine learning is based on context semantic understanding, reasonable semantic association is returned through to user input term, the retrieval result of matching is expanded and the retrieval result of reasoning, and the definition of combination expertise, effectively improve information retrieval precision and depth, realize knowledge navigation.Suitable for medical science-special topic knowledge mapping, knowledge question and content recommendation system.
Description
Technical field
It is based on the present invention relates to the technical fields such as text semantic analysis, intelligent retrieval and machine learning, more particularly to one kind
Knowledge Q-A system and intelligent search method that Wiki semantic networks build.
Background technology
Contemporary society's information content is increased sharply, and medical domain faces the frequent change of knowledge.In face of massive medical knowledge, either
Newly entering healthcare practitioners or senior medical expert has professional knowledge query demand higher.Traditional search engine retrieving from
The content for covering can not all meet requirement of the specialist medical knowledge retrieval to preciseness, the degree of accuracy and the degree of correlation to retrieval technique.
A kind of effective solution towards professional knowledge query demand is industry field ontology knowledge base.Body, both structure
Into association area vocabulary basic terms and its between relation, and explanation these words constituted using these terms and relation
The rule of remittance extension.Body is widely used in artificial intelligence, natural language processing and intelligence as the normalized form of knowledge description
In energy information system.Ontological concept is applied to ontology knowledge base the tissue of domain-specific knowledge data, is played efficient association and is known
Know concept, build knowledge network, and around knowledge concepts and network collection and tissue areas related data, solve domain knowledge
The convenience of the relativity problem of retrieval, retrieval result accuracy and knowledge acquisition still depends on the optimization of search method.
The retrieval technique of current main flow is based on the matching of keyword, and retrieval request is sent by user in the form of keyword,
Then existing document in matching database, retrieval result is returned by hit condition of key words co-occurrence.It is this complete based on keyword
Word or the retrieval mode of part matching do not consider semantic association between word, it is impossible to process synonymous, Ambiguity.Lifting retrieval is accurate
A kind of method of rate and recall ratio is to introduce semantic retrieval.Semantic retrieval is a kind of Knowledge based engineering analysis retrieval, generally certainly
By statistical model, computational linguistics application on the basis of right language understanding, with reference to artificial intelligence technology and natural language processing
Technology, from the information that the angle analysis information resources of semantic understanding are asked with user search, and completes under knowledge connection model
Retrieval.Semantic retrieval has carried out the treatment of semantic level for querying condition, shows as semantic extension, reaches precision ratio higher
And recall ratio.The essence of semantic retrieval is to carry out semantic processes improvement to conventional retrieval process, is broadly divided into user search word
Semantic processes and the semantic processes to database purchase content.
Treatment for user input term is mainly and carries out that term is synonymous and ambiguity extension, then to the inspection after extension
Rope word synonym, ambiguity set of words are retrieved again after carrying out the logical combination of retrieval type, thus expand, constrained matching condition,
So as to lift returning result accuracy rate.Treatment for data-base content is mainly and first carries out theme, label to data-base content
Classification, what is matched with user search word is no longer simple raw text content, but combines theme, label and original
The growth data of text.But either which kind of processing method is all simply extended to matching range, the semantic pass being related to
Connection rule is to be manually set rather than machine finds automatically, from terms for the treatment of effeciency, process range and speed is constrained, in face of large-scale number
According to storehouse and renewal of knowledge frequency industry field high, the hardly possible realization of real-time update of semantic rules；From in terms of improvement degree,
Synonym, the matching of ambiguity word are the extending transversely of lexical semantic, and the unrealized semanteme understood expressed by user's keyword, it is impossible to
Carry out tacit knowledge discovery or user search intent inference.
The content of the invention
The invention aims to solve shortcoming present in prior art, and the one kind for proposing is based on Wiki semantic nets
Knowledge Q-A system and intelligent search method that network builds.
To achieve these goals, present invention employs following technical scheme：
A kind of knowledge Q-A system built based on Wiki semantic networks, it is characterised in that including：
Basic data module, typing, format specification and the hierarchical structure displaying for basic data；
Intelligent processing module, including body theme predefines unit, thematic knowledge unit and relation recognition extracting unit and pushes away
Reason rule generating unit.
A kind of above-mentioned knowledge Q-A system built based on Wiki semantic networks, the basic data module includes Wiki
Grand attribute definition unit, Wiki Knowledge Elements typing unit and ontology navigation unit, wherein, data based on ontology navigation unit
Exposition, be the knowledge meta-directory with strata system, the exercise question that its strata system is defined according to Knowledge Element in typing
Rank generation.
A kind of above-mentioned knowledge Q-A system built based on Wiki semantic networks, the intelligent processing module has been used for
Into：
The structure of theme reasoning semantic model and optimization；
Model effect is played using model characteristics to be navigated with scene type hierarchical subject；
Similar theme and extension theme bifurcated tree based on semantic reasoning, and the theme defined with expertise are given birth to jointly
Into the context of co-text of retrieval/knowledge question；
Provide the user interim customization linguistic context.
The intelligent search method of above-mentioned a kind of knowledge Q-A system built based on Wiki semantic networks, comprising following step
Suddenly：
A) predefined searching motif classification is set；
B) user search word is received；
C) semantic understanding is carried out to term；
D) contents semantic retrieval is carried out according to search condition；
E) semantic parsing is carried out to the content results for retrieving；
F) retrieval result and retrieval inference of intention candidate word are returned；
G) user selects corresponding reasoning candidate word to start new round retrieval.
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step c) and bag
Include following steps：
C1) calculate term and be based on similarity degree and language construction paradigmatic relation, generate synonym/near synonym set；
C2 c1) is connected with logical "or") in synonym/near synonym set generation search condition.
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step d) and bag
Include following steps：
D1) to c1) in each word carry out similarity mode retrieval, return to hit text sentence；
D2) according to language construction syntagmatic to c1) in each word carry out the context of co-text degree of correlation matching retrieval, return
Hit text sentence.
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step e) and bag
Include following steps：
E1) extract d1) in term polymerization degree of association word higher be " similar theme " result Candidate Set；
E2) extract d2) in retrieval word combination degree of association word higher as " extension theme " result Candidate Set；
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step f) and bag
Include following steps：
F1) return to d1) and place text summary as " matching ", " approximate match " retrieval result, return d2) it is and affiliated
The summary of text is used as " recommendation ", " semantic matches " retrieval result.
F2 e1) is returned) as " similar theme " result in retrieval inference of intention, return to e2) as retrieval inference of intention
In " extension theme " result.
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step g) and bag
Containing following steps：
G1) semantic reasoning candidate word selected by user is used as new term repeat step c1) generation synonym/near synonym collection
Close, while including the original term of user input in step b)；
G2 c1) is connected with logical "or") in synonym/near synonym set generation retrieval type；
G3) with logical "and" connect g2) generation retrieval type form final retrieval type；
G4) repeat step d) to step g) is satisfied with retrieval result until obtaining.
Beneficial effects of the present invention：The present invention sets up Ontology and understands mould by deep learning Algorithm for Training corpus
Ontologies in user input term and storehouse are carried out the semantic understanding of longitudinal polymerization relation and transverse combination relation by type, are returned
Back through reasonable semantic association, expand the retrieval result of matching and the retrieval result of reasoning, and combine expertise or artificial fixed
The auxiliary of justice, lifts knowledge navigation effect, realizes the medical ontology knowledge base intelligent Search Technique based on semantic reasoning, this hair
It is bright to combine automatic theme reasoning and expertise orientation two aspects of optimization that machine learning is based on context semantic understanding
Advantage, effectively improve information retrieval precision and depth, realize knowledge navigation.Suitable for medical science-special topic knowledge mapping, know
Know question and answer and content recommendation system.
Brief description of the drawings
Fig. 1 is theory diagram of the invention；
Fig. 2 is progressive guiding intelligent search method flow chart of the invention.
Specific embodiment
The technical scheme in the embodiment of the present invention is clearly and completely illustrated below, it is clear that described embodiment
Only a part of embodiment of the invention, rather than whole embodiments.
As shown in figure 1, the invention provides a kind of knowledge Q-A system built based on Wiki semantic networks, the knowledge is asked
The system of answering includes：
Basic data module, typing, format specification and the hierarchical structure displaying for basic data；
Intelligent processing module, including body theme predefines unit, thematic knowledge unit and relation recognition extracting unit and pushes away
Reason rule generating unit.
A kind of above-mentioned knowledge Q-A system built based on Wiki semantic networks, the basic data module includes Wiki
Grand attribute definition unit, Wiki Knowledge Elements typing unit and ontology navigation unit, wherein, data based on ontology navigation unit
Exposition, be the knowledge meta-directory with strata system, the exercise question that its strata system is defined according to Knowledge Element in typing
Rank generation.
A kind of above-mentioned knowledge Q-A system built based on Wiki semantic networks, the intelligent processing module has been used for
Into：
The structure of theme reasoning semantic model and optimization；
Model effect is played using model characteristics to be navigated with scene type hierarchical subject；
Similar theme and extension theme bifurcated tree based on semantic reasoning, and the theme defined with expertise are given birth to jointly
Into the context of co-text of retrieval/knowledge question；
Provide the user interim customization linguistic context.
As shown in Fig. 2 a kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks,
Comprise the steps of：
A) predefined searching motif classification is set；
B) user search word is received；
C) semantic understanding is carried out to term；
D) contents semantic retrieval is carried out according to search condition；
E) semantic parsing is carried out to the content results for retrieving；
F) retrieval result and retrieval inference of intention candidate word are returned；
G) user selects corresponding reasoning candidate word to start new round retrieval.
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step c) and bag
Include following steps：
C1) calculate term and be based on similarity degree and language construction paradigmatic relation, generate synonym/near synonym set；
C2 c1) is connected with logical "or") in synonym/near synonym set generation search condition.
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step d) and bag
Include following steps：
D1) to c1) in each word carry out similarity mode retrieval, return to hit text sentence；
D2) according to language construction syntagmatic to c1) in each word carry out the context of co-text degree of correlation matching retrieval, return
Hit text sentence.
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step e) and bag
Include following steps：
E1) extract d1) in term polymerization degree of association word higher be " similar theme " result Candidate Set；
E2) extract d2) in retrieval word combination degree of association word higher as " extension theme " result Candidate Set；
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step f) and bag
Include following steps：
F1) return to d1) and place text summary as " matching ", " approximate match " retrieval result, return d2) it is and affiliated
The summary of text is used as " recommendation ", " semantic matches " retrieval result.
F2 e1) is returned) as " similar theme " result in retrieval inference of intention, return to e2) as retrieval inference of intention
In " extension theme " result.
A kind of intelligent search method of above-mentioned knowledge Q-A system built based on Wiki semantic networks, step g) and bag
Containing following steps：
G1) semantic reasoning candidate word selected by user is used as new term repeat step c1) generation synonym/near synonym collection
Close, while including the original term of user input in step b)；
G2 c1) is connected with logical "or") in synonym/near synonym set generation retrieval type；
G3) with logical "and" connect g2) generation retrieval type form final retrieval type；
G4) repeat step d) to step g) is satisfied with retrieval result until obtaining.
The above, the only present invention preferably specific embodiment, but protection scope of the present invention is not limited thereto,
Any one skilled in the art the invention discloses technical scope in, technology according to the present invention scheme and its
Inventive concept is subject to equivalent or change, should all be included within the scope of the present invention.
Claims (9)
1. it is a kind of based on Wiki semantic networks build knowledge Q-A system, it is characterised in that including：
Basic data module, typing, format specification and the hierarchical structure displaying for basic data；
Intelligent processing module, including the predefined unit of body theme, thematic knowledge unit and relation recognition extracting unit and reasoning rule
Then generation unit.
2. it is according to claim 1 it is a kind of based on Wiki semantic networks build knowledge Q-A system, it is characterised in that institute
State basic data module and include the grand attribute definition units of Wiki, Wiki Knowledge Elements typing unit and ontology navigation unit, wherein, this
The exposition of data based on body navigation elements, is the knowledge meta-directory with strata system, and its strata system is according to knowing
Know the rank generation of the exercise question that unit defines in typing.
3. it is according to claim 1 it is a kind of based on Wiki semantic networks build knowledge Q-A system, it is characterised in that institute
Intelligent processing module is stated for completing：
The structure of theme reasoning semantic model and optimization；
Model effect is played using model characteristics to be navigated with scene type hierarchical subject；
Similar theme and extension theme bifurcated tree based on semantic reasoning, and the theme defined with expertise generate inspection jointly
The context of co-text of rope/knowledge question；
Provide the user interim customization linguistic context.
4. it is according to claim 1 it is a kind of based on Wiki semantic networks build knowledge Q-A system intelligent retrieval side
Method, it is characterised in that comprise the steps of：
A) predefined searching motif classification is set；
B) user search word is received；
C) semantic understanding is carried out to term；
D) contents semantic retrieval is carried out according to search condition；
E) semantic parsing is carried out to the content results for retrieving；
F) retrieval result and retrieval inference of intention candidate word are returned；
G) user selects corresponding reasoning candidate word to start new round retrieval.
5. it is according to claim 4 it is a kind of based on Wiki semantic networks build knowledge Q-A system intelligent retrieval side
Method, it is characterised in that step c) and comprise the following steps：
C1) calculate term and be based on similarity degree and language construction paradigmatic relation, generate synonym/near synonym set；
C2 c1) is connected with logical "or") in synonym/near synonym set generation search condition.
6. it is according to claim 4 it is a kind of based on Wiki semantic networks build knowledge Q-A system intelligent retrieval side
Method, it is characterised in that step d) and comprise the following steps：
D1) to c1) in each word carry out similarity mode retrieval, return to hit text sentence；
D2) according to language construction syntagmatic to c1) in each word carry out the context of co-text degree of correlation matching retrieval, return hit
Text sentence.
7. it is according to claim 4 it is a kind of based on Wiki semantic networks build knowledge Q-A system intelligent retrieval side
Method, it is characterised in that step e) and comprise the following steps：
E1) extract d1) in term polymerization degree of association word higher be " similar theme " result Candidate Set；
E2) extract d2) in retrieval word combination degree of association word higher as " extension theme " result Candidate Set.
8. it is according to claim 4 it is a kind of based on Wiki semantic networks build knowledge Q-A system intelligent retrieval side
Method, it is characterised in that step f) and comprise the following steps：
F1) return to d1) and place text summary as " matching ", " approximate match " retrieval result, return d2) and affiliated text
Summary as " recommendation ", " semantic matches " retrieval result.
F2 e1) is returned) as " similar theme " result in retrieval inference of intention, return to e2) as in retrieval inference of intention
" extension theme " result.
9. it is according to claim 3 it is a kind of based on Wiki semantic networks build knowledge Q-A system intelligent retrieval side
Method, it is characterised in that step g) and comprise the steps of：
G1) semantic reasoning candidate word selected by user is used as new term repeat step c1) generation synonym/near synonym set,
Include the original term of user input in step b) simultaneously；
G2 c1) is connected with logical "or") in synonym/near synonym set generation retrieval type；
G3) with logical "and" connect g2) generation retrieval type form final retrieval type；
G4) repeat step d) to step g) is satisfied with retrieval result until obtaining.
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Cited By (13)
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| CN107679062A (en) * | 2017-07-31 | 2018-02-09 | 石河子大学 | The method and electronic equipment that a kind of reasoning colony is intended to |
| CN108108449A (en) * | 2017-12-27 | 2018-06-01 | 哈尔滨福满科技有限责任公司 | A kind of implementation method based on multi-source heterogeneous data question answering system and the system towards medical field |
| CN108549667A (en) * | 2018-03-23 | 2018-09-18 | 绍兴诺雷智信息科技有限公司 | A kind of semantic retrieving method of structuring engineering design knowledge |
| CN108897857A (en) * | 2018-06-28 | 2018-11-27 | 东华大学 | The Chinese Text Topic sentence generating method of domain-oriented |
| CN109918436A (en) * | 2019-03-08 | 2019-06-21 | 上海一健事信息科技有限公司 | A kind of Medical Knowledge management and inquiry system |
| CN110008326A (en) * | 2019-04-01 | 2019-07-12 | 苏州思必驰信息科技有限公司 | Knowledge abstraction generating method and system in conversational system |
| CN110532354A (en) * | 2019-08-27 | 2019-12-03 | 腾讯科技（深圳）有限公司 | The search method and device of content |
| CN111680173A (en) * | 2020-05-31 | 2020-09-18 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | CMR model for uniformly retrieving cross-media information |
| CN111949855A (en) * | 2020-07-31 | 2020-11-17 | 国网上海市电力公司 | Knowledge map-based engineering technology knowledge retrieval platform and method thereof |
| CN112559734A (en) * | 2019-09-26 | 2021-03-26 | 中国科学技术信息研究所 | Presentation generation method and device, electronic equipment and computer readable storage medium |
| CN112836030A (en) * | 2021-01-29 | 2021-05-25 | 成都视海芯图微电子有限公司 | Intelligent dialogue system and method |
| CN113254588A (en) * | 2021-06-02 | 2021-08-13 | 竹间智能科技（上海）有限公司 | Data searching method and system |
| CN116089628A (en) * | 2023-02-14 | 2023-05-09 | 成都市城市建设和自然资源档案馆 | City construction and natural resource archive knowledge graph construction method |
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| CN107679062A (en) * | 2017-07-31 | 2018-02-09 | 石河子大学 | The method and electronic equipment that a kind of reasoning colony is intended to |
| CN108108449A (en) * | 2017-12-27 | 2018-06-01 | 哈尔滨福满科技有限责任公司 | A kind of implementation method based on multi-source heterogeneous data question answering system and the system towards medical field |
| CN108549667A (en) * | 2018-03-23 | 2018-09-18 | 绍兴诺雷智信息科技有限公司 | A kind of semantic retrieving method of structuring engineering design knowledge |
| CN108549667B (en) * | 2018-03-23 | 2022-04-08 | 绍兴诺雷智信息科技有限公司 | Semantic retrieval method for structural engineering design knowledge |
| CN108897857B (en) * | 2018-06-28 | 2021-08-27 | 东华大学 | Chinese text subject sentence generating method facing field |
| CN108897857A (en) * | 2018-06-28 | 2018-11-27 | 东华大学 | The Chinese Text Topic sentence generating method of domain-oriented |
| CN109918436A (en) * | 2019-03-08 | 2019-06-21 | 上海一健事信息科技有限公司 | A kind of Medical Knowledge management and inquiry system |
| CN109918436B (en) * | 2019-03-08 | 2022-12-20 | 麦博(上海)健康科技有限公司 | Medical knowledge management and query system |
| CN110008326A (en) * | 2019-04-01 | 2019-07-12 | 苏州思必驰信息科技有限公司 | Knowledge abstraction generating method and system in conversational system |
| CN110532354B (en) * | 2019-08-27 | 2023-01-06 | 腾讯科技（深圳）有限公司 | Content retrieval method and device |
| CN110532354A (en) * | 2019-08-27 | 2019-12-03 | 腾讯科技（深圳）有限公司 | The search method and device of content |
| CN112559734A (en) * | 2019-09-26 | 2021-03-26 | 中国科学技术信息研究所 | Presentation generation method and device, electronic equipment and computer readable storage medium |
| CN112559734B (en) * | 2019-09-26 | 2023-10-17 | 中国科学技术信息研究所 | Brief report generating method, brief report generating device, electronic equipment and computer readable storage medium |
| CN111680173A (en) * | 2020-05-31 | 2020-09-18 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | CMR model for uniformly retrieving cross-media information |
| CN111680173B (en) * | 2020-05-31 | 2024-02-23 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | CMR model for unified searching cross-media information |
| CN111949855A (en) * | 2020-07-31 | 2020-11-17 | 国网上海市电力公司 | Knowledge map-based engineering technology knowledge retrieval platform and method thereof |
| CN112836030A (en) * | 2021-01-29 | 2021-05-25 | 成都视海芯图微电子有限公司 | Intelligent dialogue system and method |
| CN112836030B (en) * | 2021-01-29 | 2023-04-25 | 成都视海芯图微电子有限公司 | Intelligent dialogue system and method |
| CN113254588A (en) * | 2021-06-02 | 2021-08-13 | 竹间智能科技（上海）有限公司 | Data searching method and system |
| CN113254588B (en) * | 2021-06-02 | 2023-08-22 | 竹间智能科技（上海）有限公司 | Data searching method and system |
| CN116089628A (en) * | 2023-02-14 | 2023-05-09 | 成都市城市建设和自然资源档案馆 | City construction and natural resource archive knowledge graph construction method |
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Application publication date: 20170704 |